Overview

Dataset statistics

Number of variables20
Number of observations4250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory664.2 KiB
Average record size in memory160.0 B

Variable types

Text1
Numeric15
Categorical1
Boolean3

Alerts

number_vmail_messages is highly overall correlated with voice_mail_planHigh correlation
total_day_charge is highly overall correlated with total_day_minutesHigh correlation
total_day_minutes is highly overall correlated with total_day_chargeHigh correlation
total_eve_charge is highly overall correlated with total_eve_minutesHigh correlation
total_eve_minutes is highly overall correlated with total_eve_chargeHigh correlation
total_intl_charge is highly overall correlated with total_intl_minutesHigh correlation
total_intl_minutes is highly overall correlated with total_intl_chargeHigh correlation
total_night_charge is highly overall correlated with total_night_minutesHigh correlation
total_night_minutes is highly overall correlated with total_night_chargeHigh correlation
voice_mail_plan is highly overall correlated with number_vmail_messagesHigh correlation
international_plan is highly imbalanced (55.3%)Imbalance
number_vmail_messages has 3139 (73.9%) zerosZeros
number_customer_service_calls has 886 (20.8%) zerosZeros

Reproduction

Analysis started2024-04-11 18:26:38.073713
Analysis finished2024-04-11 18:27:45.124480
Duration1 minute and 7.05 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

state
Text

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:45.376531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8500
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowNJ
3rd rowOH
4th rowOK
5th rowMA
ValueCountFrequency (%)
wv 139
 
3.3%
mn 108
 
2.5%
id 106
 
2.5%
al 101
 
2.4%
va 100
 
2.4%
or 99
 
2.3%
tx 98
 
2.3%
ut 97
 
2.3%
ny 96
 
2.3%
nj 96
 
2.3%
Other values (41) 3210
75.5%
2024-04-11T18:27:46.153443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 921
 
10.8%
A 880
 
10.4%
M 779
 
9.2%
I 675
 
7.9%
T 528
 
6.2%
D 486
 
5.7%
O 432
 
5.1%
C 431
 
5.1%
V 408
 
4.8%
W 408
 
4.8%
Other values (14) 2552
30.0%

account_length
Real number (ℝ)

Distinct215
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.23624
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:46.615692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.45
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.698401
Coefficient of variation (CV)0.3960484
Kurtosis-0.13217477
Mean100.23624
Median Absolute Deviation (MAD)27
Skewness0.12232732
Sum426004
Variance1575.963
MonotonicityNot monotonic
2024-04-11T18:27:47.100166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 53
 
1.2%
87 51
 
1.2%
93 50
 
1.2%
105 48
 
1.1%
100 48
 
1.1%
120 48
 
1.1%
116 47
 
1.1%
98 47
 
1.1%
127 47
 
1.1%
112 46
 
1.1%
Other values (205) 3765
88.6%
ValueCountFrequency (%)
1 7
0.2%
2 2
 
< 0.1%
3 7
0.2%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 5
0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 3
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 2
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
222 2
< 0.1%
221 1
 
< 0.1%
217 3
0.1%
216 1
 
< 0.1%
215 1
 
< 0.1%
212 1
 
< 0.1%

area_code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
area_code_415
2108 
area_code_408
1086 
area_code_510
1056 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters55250
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarea_code_415
2nd rowarea_code_415
3rd rowarea_code_408
4th rowarea_code_415
5th rowarea_code_510

Common Values

ValueCountFrequency (%)
area_code_415 2108
49.6%
area_code_408 1086
25.6%
area_code_510 1056
24.8%

Length

2024-04-11T18:27:47.506278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T18:27:47.859622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
area_code_415 2108
49.6%
area_code_408 1086
25.6%
area_code_510 1056
24.8%

Most occurring characters

ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8500
15.4%
e 8500
15.4%
_ 8500
15.4%
r 4250
7.7%
c 4250
7.7%
o 4250
7.7%
d 4250
7.7%
4 3194
 
5.8%
1 3164
 
5.7%
5 3164
 
5.7%
Other values (2) 3228
 
5.8%

international_plan
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3854 
True
396 
ValueCountFrequency (%)
False 3854
90.7%
True 396
 
9.3%
2024-04-11T18:27:48.233151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

voice_mail_plan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3138 
True
1112 
ValueCountFrequency (%)
False 3138
73.8%
True 1112
 
26.2%
2024-04-11T18:27:48.664460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

number_vmail_messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6317647
Minimum0
Maximum52
Zeros3139
Zeros (%)73.9%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:49.091858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile36
Maximum52
Range52
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.439882
Coefficient of variation (CV)1.7610451
Kurtosis0.27303834
Mean7.6317647
Median Absolute Deviation (MAD)0
Skewness1.373091
Sum32435
Variance180.63043
MonotonicityNot monotonic
2024-04-11T18:27:49.519320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 3139
73.9%
31 69
 
1.6%
28 58
 
1.4%
24 57
 
1.3%
29 57
 
1.3%
33 55
 
1.3%
27 54
 
1.3%
26 53
 
1.2%
30 47
 
1.1%
32 47
 
1.1%
Other values (36) 614
 
14.4%
ValueCountFrequency (%)
0 3139
73.9%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
10 4
 
0.1%
11 2
 
< 0.1%
12 10
 
0.2%
13 3
 
0.1%
14 7
 
0.2%
15 12
 
0.3%
ValueCountFrequency (%)
52 1
 
< 0.1%
50 2
 
< 0.1%
49 3
 
0.1%
48 4
 
0.1%
47 4
 
0.1%
46 7
0.2%
45 10
0.2%
44 7
0.2%
43 13
0.3%
42 17
0.4%

total_day_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2596
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:50.012630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.59
Q1143.325
median180.45
Q3216.2
95-th percentile271.055
Maximum351.5
Range351.5
Interquartile range (IQR)72.875

Descriptive statistics

Standard deviation54.012373
Coefficient of variation (CV)0.2996366
Kurtosis-0.056709716
Mean180.2596
Median Absolute Deviation (MAD)36.6
Skewness-0.0069102298
Sum766103.3
Variance2917.3365
MonotonicityNot monotonic
2024-04-11T18:27:50.311382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.3 10
 
0.2%
180 9
 
0.2%
154 8
 
0.2%
177.1 8
 
0.2%
184.5 8
 
0.2%
209.9 7
 
0.2%
189.8 7
 
0.2%
138.7 7
 
0.2%
165.4 7
 
0.2%
174 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
2.6 1
< 0.1%
6.6 1
< 0.1%
7.2 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
29.9 1
< 0.1%
30.9 1
< 0.1%
ValueCountFrequency (%)
351.5 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%
329.8 1
< 0.1%

total_day_calls
Real number (ℝ)

Distinct120
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.907294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:50.605805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.850817
Coefficient of variation (CV)0.19869237
Kurtosis0.19359365
Mean99.907294
Median Absolute Deviation (MAD)13
Skewness-0.085812463
Sum424606
Variance394.05495
MonotonicityNot monotonic
2024-04-11T18:27:50.894811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 101
 
2.4%
95 97
 
2.3%
110 92
 
2.2%
94 92
 
2.2%
112 90
 
2.1%
102 89
 
2.1%
97 88
 
2.1%
107 87
 
2.0%
100 85
 
2.0%
101 84
 
2.0%
Other values (110) 3345
78.7%
ValueCountFrequency (%)
0 2
< 0.1%
30 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
< 0.1%
42 1
 
< 0.1%
44 4
0.1%
45 3
0.1%
46 1
 
< 0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
160 2
 
< 0.1%
158 2
 
< 0.1%
157 2
 
< 0.1%
156 3
 
0.1%
152 2
 
< 0.1%
151 6
0.1%
150 3
 
0.1%
148 6
0.1%
147 8
0.2%

total_day_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct1843
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.644682
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:51.218882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5735
Q124.365
median30.68
Q336.75
95-th percentile46.081
Maximum59.76
Range59.76
Interquartile range (IQR)12.385

Descriptive statistics

Standard deviation9.182096
Coefficient of variation (CV)0.29963097
Kurtosis-0.056584435
Mean30.644682
Median Absolute Deviation (MAD)6.225
Skewness-0.0069125262
Sum130239.9
Variance84.310888
MonotonicityNot monotonic
2024-04-11T18:27:51.509321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.18 10
 
0.2%
30.6 9
 
0.2%
26.18 8
 
0.2%
30.11 8
 
0.2%
31.37 8
 
0.2%
35.68 7
 
0.2%
32.27 7
 
0.2%
23.58 7
 
0.2%
28.12 7
 
0.2%
29.58 7
 
0.2%
Other values (1833) 4172
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
0.44 1
< 0.1%
1.12 1
< 0.1%
1.22 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
5.08 1
< 0.1%
5.25 1
< 0.1%
ValueCountFrequency (%)
59.76 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%
56.07 1
< 0.1%

total_eve_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1773
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.17391
Minimum0
Maximum359.3
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:51.793227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.2
Q1165.925
median200.7
Q3233.775
95-th percentile282.71
Maximum359.3
Range359.3
Interquartile range (IQR)67.85

Descriptive statistics

Standard deviation50.249518
Coefficient of variation (CV)0.25102931
Kurtosis0.043453202
Mean200.17391
Median Absolute Deviation (MAD)33.7
Skewness-0.030414586
Sum850739.1
Variance2525.0141
MonotonicityNot monotonic
2024-04-11T18:27:52.095543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.9 10
 
0.2%
199.7 9
 
0.2%
194 9
 
0.2%
187.5 9
 
0.2%
169.9 9
 
0.2%
195.5 8
 
0.2%
221.1 8
 
0.2%
223.5 8
 
0.2%
211.5 8
 
0.2%
230 8
 
0.2%
Other values (1763) 4164
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
22.3 1
< 0.1%
37.8 1
< 0.1%
41.7 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
47.3 1
< 0.1%
48.1 1
< 0.1%
49.2 1
< 0.1%
ValueCountFrequency (%)
359.3 1
< 0.1%
352.1 1
< 0.1%
351.6 1
< 0.1%
349.4 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
345.1 1
< 0.1%
344.9 1
< 0.1%
344 1
< 0.1%
341.3 1
< 0.1%

total_eve_calls
Real number (ℝ)

Distinct123
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.17647
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:52.389031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.908591
Coefficient of variation (CV)0.1987352
Kurtosis0.11459972
Mean100.17647
Median Absolute Deviation (MAD)13
Skewness-0.020811824
Sum425750
Variance396.352
MonotonicityNot monotonic
2024-04-11T18:27:52.679578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 98
 
2.3%
103 96
 
2.3%
91 95
 
2.2%
97 91
 
2.1%
108 88
 
2.1%
94 88
 
2.1%
96 88
 
2.1%
88 87
 
2.0%
101 86
 
2.0%
104 85
 
2.0%
Other values (113) 3348
78.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
38 1
 
< 0.1%
43 1
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
47 1
 
< 0.1%
48 6
0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 3
0.1%
153 1
 
< 0.1%
152 6
0.1%

total_eve_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct1572
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.015012
Minimum0
Maximum30.54
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:52.981124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.05
Q114.1025
median17.06
Q319.8675
95-th percentile24.031
Maximum30.54
Range30.54
Interquartile range (IQR)5.765

Descriptive statistics

Standard deviation4.271212
Coefficient of variation (CV)0.2510261
Kurtosis0.043329494
Mean17.015012
Median Absolute Deviation (MAD)2.86
Skewness-0.030387891
Sum72313.8
Variance18.243252
MonotonicityNot monotonic
2024-04-11T18:27:53.303627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 13
 
0.3%
18.79 13
 
0.3%
16.12 13
 
0.3%
15.9 12
 
0.3%
16.97 12
 
0.3%
18.96 11
 
0.3%
17.09 10
 
0.2%
16.8 10
 
0.2%
19.63 10
 
0.2%
17.54 9
 
0.2%
Other values (1562) 4137
97.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.9 1
< 0.1%
3.21 1
< 0.1%
3.54 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.02 1
< 0.1%
4.09 1
< 0.1%
4.18 1
< 0.1%
ValueCountFrequency (%)
30.54 1
< 0.1%
29.93 1
< 0.1%
29.89 1
< 0.1%
29.7 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.33 1
< 0.1%
29.32 1
< 0.1%
29.24 1
< 0.1%
29.01 1
< 0.1%

total_night_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct1757
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.52788
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:53.609046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.09
Q1167.225
median200.45
Q3234.7
95-th percentile282.71
Maximum395
Range395
Interquartile range (IQR)67.475

Descriptive statistics

Standard deviation50.353548
Coefficient of variation (CV)0.25110497
Kurtosis0.11485358
Mean200.52788
Median Absolute Deviation (MAD)33.55
Skewness0.0084908193
Sum852243.5
Variance2535.4798
MonotonicityNot monotonic
2024-04-11T18:27:53.899256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.2 11
 
0.3%
208.9 10
 
0.2%
240 8
 
0.2%
224.7 8
 
0.2%
190.5 8
 
0.2%
228.1 8
 
0.2%
194.3 8
 
0.2%
193.6 8
 
0.2%
221.7 8
 
0.2%
169.4 8
 
0.2%
Other values (1747) 4165
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
46.7 1
< 0.1%
47.4 1
< 0.1%
50.1 2
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
381.6 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
359.9 1
< 0.1%
355.1 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%

total_night_calls
Real number (ℝ)

Distinct128
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.839529
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:54.216706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q186
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.09322
Coefficient of variation (CV)0.20125515
Kurtosis0.077218359
Mean99.839529
Median Absolute Deviation (MAD)14
Skewness0.0052731102
Sum424318
Variance403.73748
MonotonicityNot monotonic
2024-04-11T18:27:54.518904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 100
 
2.4%
99 92
 
2.2%
95 91
 
2.1%
102 90
 
2.1%
94 88
 
2.1%
91 88
 
2.1%
98 87
 
2.0%
104 87
 
2.0%
100 86
 
2.0%
109 85
 
2.0%
Other values (118) 3356
79.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 4
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
46 3
0.1%
ValueCountFrequency (%)
175 1
< 0.1%
170 1
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
160 1
< 0.1%
159 2
< 0.1%
158 2
< 0.1%
157 2
< 0.1%
156 2
< 0.1%

total_night_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct992
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0238918
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:54.852465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.3145
Q17.5225
median9.02
Q310.56
95-th percentile12.7255
Maximum17.77
Range17.77
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation2.2659218
Coefficient of variation (CV)0.2511025
Kurtosis0.11486517
Mean9.0238918
Median Absolute Deviation (MAD)1.51
Skewness0.008444754
Sum38351.54
Variance5.1344017
MonotonicityNot monotonic
2024-04-11T18:27:55.191176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 18
 
0.4%
10.8 17
 
0.4%
9.63 17
 
0.4%
8.15 17
 
0.4%
9.66 16
 
0.4%
9.76 15
 
0.4%
8.82 15
 
0.4%
10.49 15
 
0.4%
7.69 14
 
0.3%
8.57 14
 
0.3%
Other values (982) 4092
96.3%
ValueCountFrequency (%)
0 1
< 0.1%
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.1 1
< 0.1%
2.13 1
< 0.1%
2.25 2
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
17.17 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.2 1
< 0.1%
15.98 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%

total_intl_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.256071
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:55.508365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.6
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7601017
Coefficient of variation (CV)0.26911883
Kurtosis0.70295119
Mean10.256071
Median Absolute Deviation (MAD)1.8
Skewness-0.24135954
Sum43588.3
Variance7.6181615
MonotonicityNot monotonic
2024-04-11T18:27:55.820393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 75
 
1.8%
9.8 73
 
1.7%
11.4 73
 
1.7%
10.2 72
 
1.7%
10.9 71
 
1.7%
11.3 70
 
1.6%
10.1 69
 
1.6%
9.7 68
 
1.6%
10.5 66
 
1.6%
9.5 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.4 1
 
< 0.1%
1.1 2
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
< 0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19.7 2
< 0.1%
19.3 1
 
< 0.1%
19.2 1
 
< 0.1%
18.9 1
 
< 0.1%
18.5 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
< 0.1%
18 3
0.1%

total_intl_calls
Real number (ℝ)

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4263529
Minimum0
Maximum20
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:56.113815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4630691
Coefficient of variation (CV)0.55645565
Kurtosis3.2632275
Mean4.4263529
Median Absolute Deviation (MAD)1
Skewness1.3601222
Sum18812
Variance6.0667095
MonotonicityNot monotonic
2024-04-11T18:27:56.383530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 847
19.9%
4 795
18.7%
2 644
15.2%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
1 226
 
5.3%
8 153
 
3.6%
9 126
 
3.0%
10 59
 
1.4%
Other values (11) 122
 
2.9%
ValueCountFrequency (%)
0 22
 
0.5%
1 226
 
5.3%
2 644
15.2%
3 847
19.9%
4 795
18.7%
5 598
14.1%
6 408
9.6%
7 272
 
6.4%
8 153
 
3.6%
9 126
 
3.0%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 4
 
0.1%
17 1
 
< 0.1%
16 7
 
0.2%
15 9
 
0.2%
14 5
 
0.1%
13 16
0.4%
12 18
0.4%
11 38
0.9%

total_intl_charge
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7696541
Minimum0
Maximum5.4
Zeros22
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:56.673203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.94
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.74520414
Coefficient of variation (CV)0.26906036
Kurtosis0.70332127
Mean2.7696541
Median Absolute Deviation (MAD)0.48
Skewness-0.24167067
Sum11771.03
Variance0.5553292
MonotonicityNot monotonic
2024-04-11T18:27:56.982732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 75
 
1.8%
2.65 73
 
1.7%
3.08 73
 
1.7%
2.75 72
 
1.7%
2.94 71
 
1.7%
3.05 70
 
1.6%
2.73 69
 
1.6%
2.62 68
 
1.6%
2.84 66
 
1.6%
2.57 66
 
1.6%
Other values (158) 3547
83.5%
ValueCountFrequency (%)
0 22
0.5%
0.11 1
 
< 0.1%
0.3 2
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
< 0.1%
0.57 2
 
< 0.1%
0.59 2
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.32 2
< 0.1%
5.21 1
 
< 0.1%
5.18 1
 
< 0.1%
5.1 1
 
< 0.1%
5 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
< 0.1%
4.86 3
0.1%

number_customer_service_calls
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5590588
Minimum0
Maximum9
Zeros886
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size33.3 KiB
2024-04-11T18:27:57.271560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3114335
Coefficient of variation (CV)0.84117001
Kurtosis1.6556188
Mean1.5590588
Median Absolute Deviation (MAD)1
Skewness1.0826916
Sum6626
Variance1.7198579
MonotonicityNot monotonic
2024-04-11T18:27:57.493177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1524
35.9%
2 947
22.3%
0 886
20.8%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 886
20.8%
1 1524
35.9%
2 947
22.3%
3 558
 
13.1%
4 209
 
4.9%
5 81
 
1.9%
6 28
 
0.7%
7 13
 
0.3%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 13
 
0.3%
6 28
 
0.7%
5 81
 
1.9%
4 209
 
4.9%
3 558
 
13.1%
2 947
22.3%
1 1524
35.9%
0 886
20.8%

churn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
False
3652 
True
598 
ValueCountFrequency (%)
False 3652
85.9%
True 598
 
14.1%
2024-04-11T18:27:57.760896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2024-04-11T18:27:39.737420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:40.274097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:43.884566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:47.651067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:52.922060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:56.713276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:00.681146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:05.084975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:09.447211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:13.495693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:17.468022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:22.394628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:26.588407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:30.531046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:35.934379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:39.982001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:40.520651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:44.111600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:47.881322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:53.164432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:56.950566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:00.912543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:05.421437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:09.686437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:13.750420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:17.854994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:22.629333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:26.822877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:30.773324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:36.171798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:40.246641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:40.741017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:44.353405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:48.124698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:53.395350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:57.197286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:01.162649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:05.790753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:09.917378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:13.986132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:18.218883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:22.884304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:27.092781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:31.023003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:36.428401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:40.508591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:40.996369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:44.590977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:48.368305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:53.654541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:57.433906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:01.411319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:06.152481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:10.169356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:14.244429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:18.577272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:23.131688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:27.359501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:31.326534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:36.695828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:40.770576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:41.236170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:44.812603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:48.628397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:53.916037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:57.677650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:01.651510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:06.496039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:10.423611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:14.500793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:18.955276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:23.384420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:27.607965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:31.636134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:36.942597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:41.455306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:41.467459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:45.234468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:49.015480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:54.158873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:57.929698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:01.899700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:06.920542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:10.659845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:14.753363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:19.223869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:23.634660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:27.865298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:32.012997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:37.184758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:41.721150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:41.715843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:45.487043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:49.333830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:54.404166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:58.171230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:02.155888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:07.211187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:10.903659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:15.016222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:19.609560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:23.899595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:28.136249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:32.428822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:37.446698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:41.984247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:41.957409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:45.720279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:49.670986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:54.650217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:58.429929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:02.417490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:07.460829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:11.457641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:15.271959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:19.951047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:24.151047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:28.404951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:32.849789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:37.693979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:42.222789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:42.189895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:45.944433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:50.079432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:54.916111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:58.677925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:02.668946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:07.690526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:11.719467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:15.541331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:20.237532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:24.789554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:28.673499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:33.276592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:37.929478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:42.486342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:42.447196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:46.194929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:50.508067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:55.186205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:58.957080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:02.952496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:07.940374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:11.990496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:15.810566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:20.620877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:25.056704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:28.942430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:33.690443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:38.192859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:42.750395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:42.689951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:46.452467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:50.892067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:55.444633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:59.222751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:03.359574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:08.192154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:12.262670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:16.072580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:21.027116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:25.322057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:29.227064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:34.103773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:38.444982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:43.003782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:42.933882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:46.694859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:51.272184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:55.701645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:59.480543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:03.748624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:08.461656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:12.528555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:16.331709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:21.398374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:25.579286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:29.484428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:34.520095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:38.718455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:43.262597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:43.192443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:46.947582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:51.637567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:55.989280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:59.727591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:04.107421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:08.723362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:12.778470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:16.602062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:21.653549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:25.836768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:29.745008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:34.910032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:38.988975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:43.530925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:43.446708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:47.197452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:51.968318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:56.243549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:59.985221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:04.485396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:08.975435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:13.024595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:16.863431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:21.924268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:26.108658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:30.017730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:35.295227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:39.256001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:43.777273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:43.670770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:47.429519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:52.348509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:26:56.481358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:00.215114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:04.773350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:09.210978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:13.260252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:17.121273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:22.161166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:26.354132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:30.277175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:35.680334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-11T18:27:39.496538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-11T18:27:57.993504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
account_lengtharea_codechurninternational_plannumber_customer_service_callsnumber_vmail_messagestotal_day_callstotal_day_chargetotal_day_minutestotal_eve_callstotal_eve_chargetotal_eve_minutestotal_intl_callstotal_intl_chargetotal_intl_minutestotal_night_callstotal_night_chargetotal_night_minutesvoice_mail_plan
account_length1.0000.0080.0000.000-0.007-0.0030.0190.0000.0000.005-0.014-0.0140.0190.0120.012-0.001-0.007-0.0070.000
area_code0.0081.0000.0000.0190.013-0.001-0.0070.0080.008-0.016-0.000-0.000-0.0060.0150.0150.0220.0210.0220.000
churn0.0000.0001.0000.2570.152-0.1080.0140.1770.177-0.0090.0750.075-0.0530.0480.048-0.0130.0470.0470.113
international_plan0.0000.0190.2571.000-0.0160.0030.0120.0350.035-0.0040.0180.0180.0030.0210.0210.006-0.024-0.0240.000
number_customer_service_calls-0.0070.0130.152-0.0161.000-0.024-0.021-0.007-0.0070.010-0.014-0.014-0.007-0.018-0.018-0.001-0.023-0.0230.038
number_vmail_messages-0.003-0.001-0.1080.003-0.0241.000-0.0110.0070.0070.0040.0150.015-0.0050.0020.0020.0060.0110.0110.998
total_day_calls0.019-0.0070.0140.012-0.021-0.0111.0000.0030.0030.0100.0090.0090.0080.0060.0060.0030.0020.0020.000
total_day_charge0.0000.0080.1770.035-0.0070.0070.0031.0001.0000.005-0.015-0.015-0.000-0.024-0.024-0.0040.0020.0020.036
total_day_minutes0.0000.0080.1770.035-0.0070.0070.0031.0001.0000.005-0.015-0.015-0.000-0.024-0.024-0.0040.0020.0020.036
total_eve_calls0.005-0.016-0.009-0.0040.0100.0040.0100.0050.0051.0000.0010.001-0.001-0.022-0.022-0.0160.0120.0120.000
total_eve_charge-0.014-0.0000.0750.018-0.0140.0150.009-0.015-0.0150.0011.0001.0000.0150.0060.0060.013-0.019-0.0190.017
total_eve_minutes-0.014-0.0000.0750.018-0.0140.0150.009-0.015-0.0150.0011.0001.0000.0150.0060.0060.013-0.019-0.0190.019
total_intl_calls0.019-0.006-0.0530.003-0.007-0.0050.008-0.000-0.000-0.0010.0150.0151.0000.0070.0070.004-0.020-0.0200.000
total_intl_charge0.0120.0150.0480.021-0.0180.0020.006-0.024-0.024-0.0220.0060.0060.0071.0001.0000.006-0.002-0.0020.000
total_intl_minutes0.0120.0150.0480.021-0.0180.0020.006-0.024-0.024-0.0220.0060.0060.0071.0001.0000.006-0.002-0.0020.000
total_night_calls-0.0010.022-0.0130.006-0.0010.0060.003-0.004-0.004-0.0160.0130.0130.0040.0060.0061.0000.0110.0110.000
total_night_charge-0.0070.0210.047-0.024-0.0230.0110.0020.0020.0020.012-0.019-0.019-0.020-0.002-0.0020.0111.0001.0000.031
total_night_minutes-0.0070.0220.047-0.024-0.0230.0110.0020.0020.0020.012-0.019-0.019-0.020-0.002-0.0020.0111.0001.0000.031
voice_mail_plan0.0000.0000.1130.0000.0380.9980.0000.0360.0360.0000.0170.0190.0000.0000.0000.0000.0310.0311.000

Missing values

2024-04-11T18:27:44.163935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T18:27:44.815965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
0OH107area_code_415noyes26161.612327.47195.510316.62254.410311.4513.733.701no
1NJ137area_code_415nono0243.411441.38121.211010.30162.61047.3212.253.290no
2OH84area_code_408yesno0299.47150.9061.9885.26196.9898.866.671.782no
3OK75area_code_415yesno0166.711328.34148.312212.61186.91218.4110.132.733no
4MA121area_code_510noyes24218.28837.09348.510829.62212.61189.577.572.033no
5MO147area_code_415yesno0157.07926.69103.1948.76211.8969.537.161.920no
6LA117area_code_408nono0184.59731.37351.68029.89215.8909.718.742.351no
7WV141area_code_415yesyes37258.68443.96222.011118.87326.49714.6911.253.020no
8IN65area_code_415nono0129.113721.95228.58319.42208.81119.4012.763.434yes
9RI74area_code_415nono0187.712731.91163.414813.89196.0948.829.152.460no
stateaccount_lengtharea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
4240AR127area_code_415noyes27157.610726.79280.64923.8575.1773.388.042.161no
4241WA80area_code_510nono0157.010126.69208.812717.75113.31095.1016.224.372no
4242MN150area_code_408nono0170.011528.90162.713813.83267.27712.028.322.240no
4243ND140area_code_510nono0244.711541.60258.610121.98231.311210.417.562.031yes
4244AZ97area_code_510nono0252.68942.94340.39128.93256.56711.548.852.381yes
4245MT83area_code_415nono0188.37032.01243.88820.72213.7799.6210.362.780no
4246WV73area_code_408nono0177.98930.24131.28211.15186.2898.3811.563.113no
4247NC75area_code_408nono0170.710129.02193.112616.41129.11045.816.971.861no
4248HI50area_code_408noyes40235.712740.07223.012618.96297.511613.399.952.672no
4249VT86area_code_415noyes34129.410222.00267.110422.70154.81006.979.3162.510no